Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 6, pp. 145-153
Methods of Earth remote sensing data analysis
N.P. Laverov
1 , V.V. Popovich
2 , L.A. Vedeshin
3 , F.R. Galiano
4
1 Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry RAS, Moscow, Russia
2 St.Petersburg Institute for Informatics and Automation RAS, Saint Petersburg, Russia
3 Space Research Institute RAS, Moscow, Russia
4 SPIIRAS-HTR&DO Ltd., Moscow, Russia
The article describes the methods of analysis of Earth remote sensing data - RSD. The urgency of the development of these methods is due to the pressing need to automate the process of deep processing of remote sensing data for operational use in solving a wide variety of tasks: monitoring of natural resources, fight against sea piracy, fires and other natural disasters, management of business or megalopolis and many of other actual tasks. A modified methods that increases efficiency of RSD analysis based on SVD is proposed. Theoretical results are confirmed with computer experiments and practical realization in RSD analysis system.
Keywords: remote sensing data, image processing, segmentation and classification, singular value decomposition
Full textReferences:
- Gal'yano F.R. Algoritm klassifikacii uchastkov poverhnosti zemli na osnove singulyarnogo razlozheniya matric (Algorithm of Earth's surface classification), Informatsionnye tekhnologii (Information technologies), No. 12, 2010, pp. 35-37.
- Kharinov, M. Zapominanie i adaptivnaya obrabotka informatsii tsifrovykh izobrazhenii (Storing and adaptive information processing of digital images), Saint-Petersburg: Izd. SPbU, 2006, 138 p.
- Kharinov, M.V. Adaptivnoe vstraivanie vodyanykh znakov po neskol'kim kanalam (Adaptive embedding of watermarks by multiple channels): Patent 2329522 RF, 20.07.2008, No. 20, 41 p.
- Kharinov M. V., Gal'yano F. R. Raspoznavanie izobrazhenii posredstvom predstavlenii v razlichnom chisle gradatsii (Images recognition by means of its representation in different number of gradations), 14 Konf. Matematicheskie metody raspoznavaniya obrazov (14 Conf. Mathematical Methods of Pattern Recognition), Proc. Conf., Moscow: MAKS Press, 2009, pp. 465-468.
- Aksoy, S., Chen C.H. Spatial Techniques for Image Classification, In: Signal and Image Processing for Remote Sensing, Boca Raton: Taylor & Francis, 2006, pp. 491-513.
- Galjano Ph., Popovich V. Intelligent Images Analysis in GIS, Proceedings of IF&GIS-2007, Berlin: Springer, 2007, 325 p.
- Golub G.H., Van Loan C.F. Matrix Computations, Baltimore: Johns Hopkins University Press, 1996, p. 728.
- Nock, R., Nielsen, F. Statistical Region Merging, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, No. 11, pp. 1–7.
- Robinson, D.J., Redding, N.J., Crisp, D.J. Implementation of a fast algorithm for segmenting SAR imagery, Edinburgh: DSTO Electronics and Surveillance Research Laboratory, 2002, 41 p.
- Sleator D.D., Tarjan R.E. Self-Adjusting Binary Search Trees, Journal of the ACM, 1985, Vol. 32, No., pp. 652–686.
- Storvik G., Fjortoft R., Solberg A.H.S. A Bayesian approach to classification of multiresolution remote sensing data, IEEE Transactions on Geoscience and Remote Sensing, 2005, Vol. 43, Issue 3, pp. 539–547.
- Tarakanov A.O., Skormin V.A., Sokolova S.P. Immunocomputing: Principles and applications, New York: Springer, 2003, 230 p.
- Tarjan R.E. A Data Structure for Dynamic Trees, Journal of Computer and System Sciences, 1983, Vol. 26, No. 3, pp. 362–391.